Analysis of Additional Degrees in Academic Plastic Surgery Faculty
Bibliographic record
Abstract
Background: As plastic surgery continues to evolve, an increasing number of surgeons are attaining additional degrees (ADs). Prior studies illustrate this trend of increased AD attainment among plastic surgery faculty within the United States. Yet, no such study has documented AD attainment variability and influence within Canadian plastic surgery faculty. Objectives: Our objective was to investigate the relationship between AD attainment and gender, alongside research productivity, and academic rank of Canadian plastic surgery faculty members. Methods: All Canadian academic plastic surgery faculty members were identified and information regarding gender, academic rank, research productivity, timing of AD attainment was recorded. AD was defined as any degree beyond a medical degree or equivalent. Results: A total of 299 faculty members were identified. Of these, 33% (N = 99) attained an AD. A higher percentage of females (40%) obtained ADs compared to males (30%) ( P = .0402). When controlling for number of years in practice, there was a significantly larger proportion of females than males with ADs as assistant and associate professor ( P = .033). Faculty with ADs were associated with higher research productivity and higher academic rank than those with MDs ( P < .05). ADs were commonly obtained post-residency (38%) and most common ADs were MSc (51%) and PhDs (21%). It was found that the Canadian plastic surgeons were less likely to pursue MBAs than US plastic surgeons ( P = .002). Conclusion: One-third of Canadian academic plastic surgeons had ADs. Those with ADs present with higher research productivity and academic rank. When segmented by gender, there were significant differences among AD holders. The results of this study will lend support to ongoing endeavors voicing the need for gender equity in academic plastic surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".